US10217032B2 - Method for choosing a compression algorithm depending on the image type - Google Patents
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- US10217032B2 US10217032B2 US15/328,772 US201515328772A US10217032B2 US 10217032 B2 US10217032 B2 US 10217032B2 US 201515328772 A US201515328772 A US 201515328772A US 10217032 B2 US10217032 B2 US 10217032B2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/64—Systems for the transmission or the storage of the colour picture signal; Details therefor, e.g. coding or decoding means therefor
- H04N1/642—Adapting to different types of images, e.g. characters, graphs, black and white image portions
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- G06K9/628—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
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- G06K9/4652—
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- G06K9/6276—
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/12—Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/136—Incoming video signal characteristics or properties
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/182—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
- H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/63—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using sub-band based transform, e.g. wavelets
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
Definitions
- the images can belong to very different types.
- Each compression algorithm uses its own data representation.
- the compression via wavelets separates the image into successive sub-images with frequency transformations, while certain codecs, in particular developed by the applicant take the differences between the numerical values of the image.
- the invention therefore proposes to define a codec that automatically selects at encoding the best representation of the data using the type of image data, and carries out the inverse transform at decompression using information contained in the file header.
- Each one of the types of algorithms is more or less adapted to certain types of images.
- frequency representations model low-contrast images very well while representations via differences model graphic or highly contrasted images well.
- Each one of the methods can be used in loss or lossless mode.
- the transformation is applied to each one of the layers separately.
- the choice of the type of transformation is taken on the layer considered to be the most representative, for example the layer Y in the case of an image that has been subjected beforehand to a YCbCr transform, or the layer that best represents the light intensity of an image in the case of a lossless colorimetric transformation.
- this transformation can be carried out by a specific implementation of wavelets and binary encoding, or using standard formats such as Jpeg2000 or PGF.
- the wavelet formats used will be Jpeg2000 and PGF.
- the transformation via differences consists in taking the difference between the values of two adjacent pixels over the same layer, then in quantifying this difference by a predefined factor Q. In order to not propagate the error, the difference is taken with respect to a decompressed value defined hereinbelow. In the same way, if two directions of differences are possible, the direction that would generate the lowest difference, using decompressed values, is determined. The difference at compression and decompression is then calculated.
- this method of encoding is carried out in the following way:
- a matrix to be transformed is considered, representing a layer of an image in 2 dimensions.
- the following nomenclature is adopted:
- Vij is an initial value of the matrix, for which i represents the line number and j the column number. Cij represents the corresponding compressed value, and Dij the corresponding decompressed value. As such, for a 5 ⁇ 5 matrix, the following is the distribution of the values:
- the differences are taken line by line, from the first to the last, from left to right.
- the first value V11 is retained as is.
- the compressed value Ci1 of the first box of said line is calculated by taking a difference between the current value Vi1 and the decompressed value of the line immediately above Di-11:
- the difference horizontally is calculated if (Di-1 j ⁇ D i ⁇ 1 j ⁇ 1) is less as an absolute value than (Di j ⁇ 1 ⁇ D i ⁇ 1 j ⁇ 1), and the difference is calculated vertically in the opposite case.
- two methods of compression via wavelets are applied, for example Jpeg2000 and PGF, as well as the compression chain APE, RLE, Bzip, described hereinabove, over 3 different images:
- FIG. 1 is a screen copy, containing much text on a white background, and represents an example of an image of the “graphics” type
- FIG. 2 is a photograph in town with high contrasts between the buildings and the sky, the lights, etc. It represents an example of an image of the “high contrast” type
- FIG. 3 is a photograph of an airshow that contains many gradients of colours. It represents an example of an image of the “low contrast” type.
- PSNR curve represents the quality of the restored image after compression then decompression.
- Each encoding parameter corresponds to a file size and a value of quality referred to as PSNR, between 0 and 100.
- the PSNR is a standard measurement, here calculated over the layer Y, with 100 being the best quality possible and corresponds to a lossless compression. It is considered that a compression has better performance than another when, at the equivalent size, it has a better PSNR, or when with an equivalent PSNR, the size is less.
- FIG. 4 and the table hereinbelow show the change in the PSNR according to the image size for the image shown in FIG. 1 .
- PSNR APE + RLE + zlib Q 80 502 100 PGF PGFConsole, ⁇ q 0 1038 100 Jpeg2000 OpenJpeg, 882 100 image_to_j2k, no parameter
- FIG. 5 and the table hereinbelow show the change in the PSNR according to the size of the image for the image shown in FIG. 2 .
- FIG. 6 and the table hereinbelow show the change in the PSNR according to the size of the image for the image shown in FIG. 3 :
- the choice of the algorithm is taken after the colorimetric transformation, YCbCr in the examples shown.
- the number of unique RGB colour triplets of the image is counted, which is reduced to the size of the image, preferably by dividing it by a coefficient according to the number of pixels of the image.
- the image is considered to be a graphics image; when it is above a second threshold, higher than the first, the image is considered to be a low contrast image. Between these two thresholds, the image is considered to be highly contrasted.
- the calculation is carried out over all of a layer that is most representative of the image (for example the layer Y)
- these steps can be preceded by a colorimetric transformation, with loss or lossless, on the input data.
- a colorimetric transformation with loss or lossless, on the input data.
- a YCbCr transformation can be applied on the RGB input data.
- the sum of the proportions of the neighbouring values is equal to one.
- the indicator of the concentration of the hues around values k (E(k)) is then maintained higher than a certain threshold, preferably the positive indicators of concentration, i.e. Max(E(k),0), and each one of the indicators of concentration is reduced to the size of the image, for example to the total number (N) of pixels of the image.
- the result Max(E(k))/N is then raised to a power strictly greater than 1, preferably equal to 2.
- a metric (FD) is then obtained by compiling these results for all of the layer, preferably by taking the sum of the results obtained as such for all of the hues of the layer.
- FD 2 ⁇ (Max( E ( k ))/ N ) 2 ,
Abstract
-
- calculating a level of hues of the image over at least all of one layer of the image;
- depending on the type of hues of the representative layer, classifying the image in one of the following three classes:
- a first class if the image is of a graphics type;
- a second class if the image is of a highly contrasted type;
- a third class if the image is of a low-contrasted type; and,
- choosing a compression processing type depending on the class of the image:
- difference processing, if the image is of the first class;
- frequency processing, if the image is of the third class; and,
- if the image is of the second class:
- for lossless or low-loss compression, preferably using difference processing, and,
- in the other cases preferably using frequency processing.
Description
V11 | V12 | V13 | V14 | V15 | C11 | C12 | C13 | C14 | C15 | D11 | D12 | D13 | D14 | D15 |
V21 | V22 | V23 | V24 | V25 | C21 | C22 | C23 | C24 | C25 | D21 | D22 | D23 | D24 | D25 |
V31 | V32 | V33 | V34 | V35 | C31 | C32 | C33 | C34 | C35 | D31 | D32 | D33 | D34 | D35 |
V41 | V42 | V43 | V44 | V45 | C41 | C42 | C43 | C44 | C45 | D41 | D42 | D43 | D44 | D45 |
V51 | V52 | V53 | V54 | V55 | C51 | C52 | C53 | C54 | C55 | D51 | D52 | D53 | D54 | D55 |
0 | 0 | 0 | 0 | 0 |
0 | 0 | 255 | 253 | 0 |
0 | 0 | 255 | 253 | 0 |
0 | 0 | 255 | 253 | 0 |
0 | 0 | 255 | 253 | 0 |
D11=C11=V11=0;
C12=ROUND((V12−D11)/Q)=ROUND((0−0)/3)=0
D12=ROUND(D11+(C12*Q))=ROUND(0+0*3)=0
C21=ROUND((V21−D11)/Q)=ROUND((0−0)/3)=0
D21=ROUND(D11+(C21*Q))=ROUND(0+(0*3))=0
-
- The absolute value of (D12-D11) is 0;
- The absolute value of (D21-D11) is 0;
- As the two values are equal, the vertical difference is chosen;
- The compressed value is therefore calculated: C22=ROUND((V22−D12)/Q)=ROUND((0=0)/3)=0
- Then the decompressed value is calculated: D22=ROUND(D12+(C22*Q))=ROUND(0+0*3)=0
-
- The absolute value of (D13-D12) is 0;
- The absolute value of (D22-D12) is 0;
- As the two values are equal, the vertical difference is chosen;
- The compressed value is therefore calculated: C23=ROUND((V23−D13)/Q)=ROUND((255−0)/3)=85
- Then the decompressed value is calculated: D23=ROUND(D13+(C23*Q))=ROUND(0+85*3)=255
-
- The absolute value of (D14-D13) is 0;
- The absolute value of (D23-D13) is 255;
- As the value of the first difference (horizontal) is the smaller, the horizontal difference is chosen;
- The compressed value is therefore calculated: C24=ROUND((V24−D23)/Q)=ROUND((253−255)/3)=−1
- Then the decompressed value is calculated: D24=ROUND(D23+(C24*Q))=ROUND(255−1*3)=252
V11 | V12 | V13 | V14 | V15 | C11 | C12 | C13 | C14 | C15 | D11 | D12 | D13 | D14 | D15 |
V21 | V22 | V23 | V24 | V25 | C21 | C22 | C23 | C24 | C25 | D21 | D22 | D23 | D24 | D25 |
V31 | V32 | V33 | V34 | V35 | C31 | C32 | C33 | C34 | C35 | D31 | D32 | D33 | D34 | D35 |
V41 | V42 | V43 | V44 | V45 | C41 | C42 | C43 | C44 | C45 | D41 | D42 | D43 | D44 | D45 |
V51 | V52 | V53 | V54 | V55 | C51 | C52 | C53 | C54 | C55 | D51 | D52 | D53 | D54 | D55 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
0 | 0 | 255 | 253 | 0 | 0 | 0 | 85 | −1 | −84 | 0 | 0 | 255 | 252 | 0 |
0 | 0 | 255 | 253 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 255 | 252 | 0 |
0 | 0 | 255 | 253 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 255 | 252 | 0 |
0 | 0 | 255 | 253 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 255 | 252 | 0 |
Programme/ | ||||
Parameters/ | ||||
Algorithm | Quantification factor | Size (kb) | PSNR | |
APE + RLE + zlib | Q = 80 | 502 | 100 | |
PGF | PGFConsole, − |
1038 | 100 | |
Jpeg2000 | OpenJpeg, | 882 | 100 | |
image_to_j2k, no | ||||
parameter | ||||
Programme/ | |||
Parameters/ | |||
Algorithm | Quantification factor | Size (kb) | PSNR |
APE + RLE + zlib | Q = 1 | 5832 | 100 |
PGF | PGFConsole, − |
7893 | 100 |
Jpeg2000 | OpenJpeg, | 7266 | 100 |
image_to_j2k, no | |||
parameter | |||
APE + RLE + zlib | Q = 22 | 1155 | 34 |
PGF | PGFConsole, − |
1212 | 43 |
Jpeg2000 | OpenJpeg, | 1112 | 44 |
image_to_j2k, no | |||
parameter | |||
Programme/ | |||
Parameters/ | |||
Algorithm | Quantification factor | Size (kb) | PSNR |
APE + RLE + zlib | Q = 1 | 15175 | 100 |
PGF | PGFConsole, − |
14303 | 100 |
Jpeg2000 | OpenJpeg, | 14165 | 100 |
image_to_j2k, no | |||
parameter | |||
-
- Encodings using wavelets have a tendency to have size/quality performance that is close, while APE obtains results that are radically different;
- In the case of image 1 (Graphics image), the APE is better in all cases;
- In the case of image 2 (highly contrasted image), the APE is better on high qualities, encoding via wavelets for the strongest compressions;
- In the case of image 3 (image with low contrast), encodings with wavelets are better in all cases.
-
- the number of each one of the values on the most representative layer (ideally Y) is counted;
- a histogram of the values is constructed such as shown in
FIG. 7 : - For each value k generally between 0 and 255, the number of times n(k) that this value is present in the layer is noted:
- The number of pixels of the layer is therefore equal to the sum of the n(k):
-
- The metric “FD2” provides an idea of the “peak” aspect of the histogram:
-
- The metric FD2 is carried out, over all or a portion of a layer of the image
- The higher FD2 is, the more concentrated the values are
Image1 | Image2 | Image3 | |||
FD2 | 0.18 | 0.00065 | 1.1E−06 | ||
-
- It is therefore easily seen that the different types of images belong to different magnitudes, and that the formula is indeed discriminatory.
- The image is separated in the following way:
- FD2>0.075: Graphics image
- FD2>10−4: Highly contrasted image
- Otherwise: Low-contrasted image
- If FD2>0.075, a transform via differences is chosen, for example APE+RLE+zlib;
- In the case of a highly contrasted image, a transform via differences is chosen, for example APE+RLE+zlib in lossless and near-lossless modes, and an encoding by wavelets in the other cases
- In the case of a low-contrasted image, encoding by wavelets is carried out in all cases, for example of the JPEG or PGF type.
- The type of image is stored in the file header
- The inverse operations are carried out at decompression depending on the image type
-
- In the case of a highly contrasted image, a difference transform is chosen, for example APE+RLE+ zlib in the lossless or near-lossless modes, an encoding by wavelets otherwise
- In the case of a low-contrast image, an encoding by wavelets is carried out in all cases, for example of the JPEG or PGF type.
- The type of image is stored in the file header
- The inverse operations to decompression are carried out depending on the image type
-
- calculating a level of hues of the image over at least all of one layer of the image,
- depending on the type of hues over at least all of one layer, classifying the image in one of the following three classes:
- a first class if the image is of a graphics type;
- a second class if the image is of a highly contrasted type;
- a third class if the image is of a weakly contrasted type; and,
- choosing a compression processing type depending on the class of the image:
- difference processing, if the image is of the first class;
- frequency processing, preferably using wavelets, if the image is of the third class; and,
- if the image is of the second class:
- for lossless or low-loss compression, preferably using difference processing, and,
- in the other cases preferably using frequency processing, preferably using wavelets.
E(k)=n(k)−0.4(n(k−1)+n(k+1))−0.1(n(k−2)+n(k+2)),
by taking the difference between the number of pixels n(k) of the hue (k) considered and a proportion of those of its neighbours, preferably of its first-row (k−1 and k+1) and second-row (k−2 and k+2) neighbours, with the respective proportion being more reduced for the neighbours of the highest row, for example a proportion of 80% for each one of the first-row neighbours, i.e. the immediate neighbours of the hue (k) considered and of 20% for each one of the second-row neighbours, i.e. the immediate neighbours of the first-row neighbours.
FD2=Σ(Max(E(k))/N)2,
-
- for k varying from 0 to 255
Claims (20)
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FR1401695A FR3024263B1 (en) | 2014-07-24 | 2014-07-24 | METHOD FOR CHOOSING A COMPRESSION ALGORITHM ACCORDING TO IMAGE TYPE |
FR14/01695 | 2014-07-24 | ||
FR1401695 | 2014-07-24 | ||
PCT/FR2015/000142 WO2016012667A1 (en) | 2014-07-24 | 2015-07-09 | Method for choosing a compression algorithm depending on the image type |
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CN108399052B (en) * | 2018-02-07 | 2021-05-14 | 深圳壹账通智能科技有限公司 | Picture compression method and device, computer equipment and storage medium |
US10776957B2 (en) | 2018-10-02 | 2020-09-15 | Samsung Electronics Co., Ltd. | Online image compression in hardware |
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- 2015-07-09 CA CA2992930A patent/CA2992930A1/en not_active Abandoned
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- 2015-07-09 WO PCT/FR2015/000142 patent/WO2016012667A1/en active Application Filing
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